Computational science has led to exciting new developments:

• Technology is increasing data collection throughput; data are more complex and highdimensional
• Existing databases can be merged to become bigger databases
• Computing power allows more sophisticated analyses, even on “small” data
• For every field “X” there is a “Computational X”

Increasing computational complexity of analyses:

has exposed limitations in our ability to evaluate published findings.

• Even basic analyses difficult to describe

• Errors more easily introduced into long analysis pipelines

• Knowledge transfer is inhibited

• Results are difficult to replicate or reproduce

• Complicated analyses cannot be trusted

calls for reproducibility

Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.

• fully scripted analyses
• make code and data available

reproducibility limitations

• top down
• downstream (post publication)

• ultimately does not address the key question:

  can we trust these results?

evidence based science

evdence needs:

• documenting
• linking
• communicating

rmarkdown can integrate tools, processes and outputs into evidence streams

at all stages of scientific process

simple tools:

low hanging fruit

• begin at the start of the process
• document & interlink evidence streams
• explore and communicate!

empower your code and data

examples

report

code documentation

method collation

interactive documents

presentations

text

    normal text

normal text

    *italic text*

italic text

    **bold text**

bold text

    **bold italic text**

bold italic text

    superscript^2^

superscript²

    ~~strikethrough~~

strikethrough

headers

unordered lists

ordered lists

quotes & code

> this text will be quoted

this text will be quoted

`this text will appear as code` inline

this text will appear as code inline

a <- 10

    the value of parameter *a* is 10

the value of parameter a is 10

text formatting

images

    ![](https://www.rstudio.com/wp-content/uploads/2015/01/rmarkdown-cheatsheet-2-e1457627578814.png)
    
    ![](resources/cheat.png)
    

resize images

    <img src="resources/cheat.png" width="200px" />

basic tables

Table Header  | Second Header
------------- | -------------
Cell 1        | Cell 2
Cell 3        | Cell 4 

online table to .md converter

links

[Download R](http://www.r-project.org/)    
[RStudio](http://www.rstudio.com/)

Download R

RStudio

.md resources

offical markdown documentation

Rmarkdown documentation

Rstudio Rmarkdown cheatsheet

github.io websites: eg Andy South’s blog

Reproducible Research coursera MOOC

Producing html documents from .R scripts using knitr::spin

R code chunks can be used as a means render R output into documents or to simply display code for illustration

options

for more details see http://yihui.name/knitr/

set default options

knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)

knitr::kable() tables

require(knitr)
data(airquality)
kable(head(airquality), caption = "New York Air Quality Measurements")

DT::kable() tables

require(DT)
data(airquality)
datatable(airquality, caption = "New York Air Quality Measurements")

plotly

library(plotly)

set.seed(100)
d <- diamonds[sample(nrow(diamonds), 1000), ]

p <- ggplot(data = d, aes(x = carat, y = price)) +
  geom_point(aes(text = paste("Clarity:", clarity)), size = 1) +
  geom_smooth(aes(colour = cut, fill = cut)) + facet_wrap(~ cut)

ggplotly(p)

shiny

outputs

rpubs

demo

your mission

create your first .Rmd!

• choose some data eg:
  • datasets package
  • data(package = .packages(all.available = TRUE))
• show us some data in a table
• plot some data
• write a bit about what you did
• publish it on rpubs. Add you link to our googledoc

see my example:

• html
• raw .Rmd